In recent years there has been considerable activity in the field of so-called ‘smart’ appliances, i.e., household and other appliances which incorporate sensing systems responsive to one or more process or environmental conditions and which operate under the control of a model algorithm so as, for example, to automate some manual function of the appliance, to economize on energy or reduce environmental impact, to optimise the ease of use and performance of the appliance or of an ancillary product, or to provide some new appliance functionality.
Smart appliance technology is finding particular application in the field of cleaning appliances including automatic laundering, dry-cleaning and dishwashing machines, both domestic and commercial. Typical sensors in use today include water level sensors in clothes washers, humidity sensors for automatic dryer control, temperature sensors, turbidity and other optical sensors for sensing wash water soil, conductivity sensors for sensing water hardness or for detecting the type of product being used, position sensors for applications such as dishwasher spray arm position, speed sensors to detect the rotational speed of the clothes drum, torque, inertia and water absorption sensors for use in load and fabric type sensing, and accelerometers to sense vibration caused by out-of-balance loads in washers.
The accurate and reliable estimation of the level and composition of substrate soil is of especial importance from the viewpoint of determining the correct product usage for achieving optimum cleaning, finishing and fabric care performance. Many consumers will decide on the correct dosage to use based on a visual assessment of the soiled items and in particular on the degree of staining of laundry items. Visual assessment often provides a false impression of the degree of soiling however. A stain may make a big visual impact but it could in practice represent a small soil load. Many common body soils on the other hand have low visibility. So often a consumer will overdose product in cases where the soil load is visible but low; or underdose product in cases where the soil load is of low visibility but high.
There is a need therefore to develop model algorithms that can provide a robust and accurate measurement of the soil load in a soil-containing liquid medium or an accurate prediction of the soil load on the soiled article for use in estimating optimum product usage levels. Soil levels and composition are difficult to measure accurately, however, in part because of their varying origin and complexity. No single parameter currently exists that can directly measure soil since it is a heterogeneous substance composed of a multitude of components having differing physical, chemical and biological properties. Using turbidity as a measure of soil, for example, could lead to an inconclusive or inaccurate assessment of the soil load because turbidity only reflects one aspect of the soil. In addition the properties and environmental history of the soil-containing system, for example the water source, water quality, hardness, substrate load, etc can also affect the measurement process and lead to a less accurate prediction of the soil by, in effect, increasing noise and lowering signal-to-noise ratio.
Another aspect of the problem is that of accurately predicting the substrate soil load prior to the cleaning operation and detergent product delivery. Typically the absolute soil level of the clothes has been determined from the steady state saturation value of a turbidity sensor signal and from the time the turbidity signal takes to reach that value from an initial condition at the start of the wash. Reaching a steady state saturation value can take a significant amount of time, however, and it would clearly be advantageous to determine predicted soil levels by a dynamic process whereby the optimum product usages can be determined prior to achievement of a steady state and prior to delivery of the detergent or ancillary products into the cleaning liquor.
Accordingly the present invention provides improved methods using soil sensor-based model algorithm techniques for measuring the soil load in a soil-containing liquid medium and for estimating the soil load on the soiled article for use in predicting optimum product usage levels and other dosage-related control parameters.